[Paper Review] A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges

Yuqi Nie, Yaxuan Kong+5 more|arXiv (Cornell University)|Jun 15, 2024

Stock Market Forecasting Methods29 citations

TL;DR

A comprehensive survey of how large language models are applied in finance, covering model types, application areas, datasets, benchmarks, and the main challenges and opportunities.

ABSTRACT

Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of data, and generating human-preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We provide a discussion of the progress and advantages of LLMs in financial contexts, analyzing their advanced technologies as well as prospective capabilities in contextual understanding, transfer learning flexibility, complex emotion detection, etc. We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications. For each application area, we delve into specific methodologies, such as textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations. Furthermore, a comprehensive collection of datasets, model assets, and useful codes associated with mainstream applications are presented as resources for the researchers and practitioners. Finally, we outline the challenges and opportunities for future research, particularly emphasizing a number of distinctive aspects in this field. We hope our work can help facilitate the adoption and further development of LLMs in the financial sector.

Motivation & Objective

• Provide a holistic view of financial LLM applications and their practical implications for researchers and practitioners.
• Catalog financial-domain LLMs, including architecture, pre-training, fine-tuning, and customization strategies.
• Summarize datasets, benchmarks, and code resources available for financial LLM research.
• Identify distinct challenges in finance (data issues, benchmarking, ethics, interpretability) and propose future directions.

Proposed method

• Classify and analyze financial-domain LLMs and their fine-tuning strategies.
• Review application areas: linguistic tasks, sentiment analysis, time series analysis, financial reasoning, and agent-based modeling.
• Summarize datasets, benchmarks, and available code for financial LLMs.
• Discuss challenges and opportunities specific to finance, including data quality, ethics, and safety.

Experimental results

Research questions

• RQ1 What are the dominant LLM architectures and fine-tuning approaches used in finance?
• RQ2 How are LLMs applied across linguistic tasks, sentiment, time series, reasoning, and agent-based modeling in finance?
• RQ3 What datasets, benchmarks, and code resources support financial LLM research?
• RQ4 What are the key challenges and open opportunities for deploying LLMs in the financial sector?

Key findings

• Financial-domain LLMs include specialized variants derived from GPT, BERT/RoBERTa, T5, ELECTRA, BLOOM and Llama families, with domain-specific adaptations.
• Applications span linguistic tasks, sentiment analysis, financial time series, financial reasoning, and agent-based modeling, with methodologies like textual analysis, forecasting, planning, and simulations.
• There is a growing collection of datasets, benchmarks, and open-code resources tailored to finance to support research and development.
• Key challenges include data quality, backtesting biases, interpretability, legal and ethical considerations, scalability, and privacy concerns.